With the advent of large-scale whole-genome variation data, population geneticists are currently interested in considering increasingly more complex models. However, statistical inference in this setting is a challenging task, as computing the likelihood of a complex population genetic model is a difficult problem both theoretically and computationally. In this paper, we introduce a novel likelihood-free inference framework for population genomics by applying deep learning, which is an active area of machine learning research. To our knowledge, deep learning has not been employed in population genomics before. A recent survey article [1] provides an accessible introduction to deep learning, and we provide a high-level description below.

When encountering novel object, humans and other animals are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with themin a goal driven way. This process of active interaction is in the same spirit of a scientist performing an experiment to discover hidden facts. The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved'superhuman performance' in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – 'it is not clear that these systems can rival the scientific intuition of even a young child.' "We found," the team concluded, "that state of art deep reinforcement learning methods can learn to perform the experiments necessary to discover these hidden properties of the physical world. By systematically manipulating the problem difficulty and the cost incurred by the AI agent for performing experiments, we found that agents learn different strategies that balance the cost of gathering information against the cost of making mistakes in different situations."

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

Since the human genome was successfully mapped in 2003, researchers have been making use of technology to organise the growing mountain of genomics data into a form that will eventually benefit actual patients. When he unveiled the Precision Medicine Initiative in 2015, US president Barack Obama recognised the potential that technology and gene mapping can bring to patients. His state of the union address laid out a vision for the groundbreaking initiative: "Doctors have always recognised that every patient is unique, and doctors have always tried to tailor their treatments as best they can to individuals. You can match a blood transfusion to a blood type - that was an important discovery. What if matching a cancer cure to our genetic code was just as easy, just as standard?"